Two deep learning models are used, one for object detection and one for semantic segmentation. For object detection, an algorithm called Retinanet [1]} is used. Here we used the following implementation in Keras [2]}: https://github.com/fizyr/keras-retinanet, which uses a Resnet50 [3]} backbone. The original images had a resolution of 2100 by 1400 pixels. For Retinanet the images were downscaled to 1050 by 700 pixels. This is necessary to fit the batch (batch size = 4) into GPU RAM.
